The ingestible event marker data framework provides a uniform, comprehensive framework to enable various functions and utilities related to ingestible event marker data (IEM data). Included are a receiver adapted to be associated with a body of an individual, the receiver configured to receive IEM data; a hub to receive the IEM data; and at least one IEM data system to receive the data from the hub. Among other information, behavioral data and predictive inferences may be provided.
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1. A system comprising: an ingestible event marker device configured to collect Ingestible Event Marker (IEM) data from a body of an individual and transmit a conductive signal comprising the IEM data via body tissue, wherein the IEM data comprise information associated with an ingestion event; a receiver adapted to be associated with the body of the individual, the receiver configured to receive the conductive signal comprising the IEM data via the body of the individual associated with the receiver, wherein the conductive signal is undetectable beyond the body; a hub to receive the IEM data from the receiver; at least one IEM data system to receive the IEM data from the hub; and wherein the at least one IEM data system analyzes the IEM data and generates at least one metric based on the IEM data; and wherein the at least one IEM data system generates predictive information based on the at least one metric, wherein the predictive information is related to prediction of a state of the individual.
The system uses an ingestible event marker (IEM) device that transmits data about ingestion events (e.g., medication) from inside the body using a conductive signal that is undetectable outside the body. A receiver worn on or near the body picks up this signal. A hub (e.g., a smartphone) receives the data from the receiver. An IEM data system then analyzes this data to generate metrics and uses those metrics to predict a state of the individual, providing behavioral data and predictive inferences.
2. The system of claim 1 , wherein the receiver is selected from a group consisting of a patch receiver, an implantable receiver, a receiver configured to be worn on the body, apparel-configured receiver, and a receiver adapted to be associated with the hub.
This system, as described in the previous claim, allows for several variations of the receiver component. The receiver that picks up the signals from the ingestible device can be a patch attached to the skin, an implanted device, a receiver worn on the body like a watch, incorporated into clothing, or attached to the hub device, like a smartphone.
3. The system of claim 2 , wherein the receiver adapted to be associated with the hub comprises a mobile phone attachment.
This system, using the patch receiver, implantable receiver, a receiver configured to be worn on the body, apparel-configured receiver, and a receiver adapted to be associated with the hub from the previous description, further specifies that when the receiver is adapted to be associated with the hub, it can be a mobile phone attachment.
4. The system of claim 3 , wherein the hub is a mobile phone.
This system, where the receiver is a mobile phone attachment, specifies that the hub (which receives data from the receiver) can be a mobile phone itself. So the receiver plugs into the phone, and the phone acts as the hub.
5. The system of claim 3 , wherein the software further comprises at least one of an analysis module, a metrics module, and a predictive information module.
This system, where the receiver is a mobile phone attachment, includes software in the IEM data system. This software is composed of modules for analysis, metrics generation, and predictive information processing. This enables the system to do the analysis, calculate the metrics, and make predictions.
6. The system of claim 5 , wherein the analysis module analyzes the IEM data.
This system, which contains an analysis module, a metrics module, and a predictive information module, has an analysis module responsible for analyzing the data coming from the ingestible event marker device.
7. The system of claim 5 , wherein the metrics modules generates the at least one metric based on the IEM data.
This system, which contains an analysis module, a metrics module, and a predictive information module, has a metrics module that generates metrics (i.e., quantifiable measurements) based on the data collected by the ingestible event marker.
8. The system of claim 5 , wherein the predictive information module generates the predictive information based on the IEM data.
This system, which contains an analysis module, a metrics module, and a predictive information module, has a predictive information module that generates predictions based on the data collected by the ingestible event marker.
9. The system of claim 5 , wherein the IEM data further comprise physiologic data, the analysis module analyzes the physiologic data and generates the at least one metric based on the physiologic data, and the predictive module generates the predictive information based on the physiologic data.
This system, which contains an analysis module, a metrics module, and a predictive information module, collects physiologic data, along with the ingestion event data. The analysis module analyzes this physiologic data (e.g. heart rate, temperature) to generate metrics. The predictive module uses these metrics to make predictions based on the physiologic data.
10. The system of claim 2 , wherein the at least one IEM data system comprises at least one of a feedback loop system and a decision support system.
This system, using the patch receiver, implantable receiver, a receiver configured to be worn on the body, apparel-configured receiver, and a receiver adapted to be associated with the hub, allows the IEM data system to either provide a feedback loop (e.g., alerting the user to take medication) or act as a decision support system (e.g., providing information to a doctor).
11. The system of claim 1 , where the at least one IEM data system further comprises software.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, further includes software in the IEM data system for processing the data.
12. The system of claim 1 , wherein the receiver is configured to capture the IEM data at a predetermined rate.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, configures the receiver to capture the IEM data at a predetermined rate (e.g., every 5 minutes).
13. The system of claim 1 , wherein the IEM data comprises information relating to a dosage of medication.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, uses data that includes information on medication dosage taken by the individual.
14. The system of claim 1 , wherein the receiver is configured to transmit additional data to the hub, and the at least one IEM data system to receive the additional data from the hub, wherein the additional data comprises data derived from the receiver.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, configures the receiver to transmit additional data to the hub (beyond just the IEM data). The IEM data system then also receives this additional data from the hub. The additional data is derived directly from the receiver.
15. The system of claim 14 , wherein the at least one metric comprises a standard deviation of the circadian pattern of the individual across a predetermined number of days and an overall variability of the circadian pattern of the individual.
This system, where the receiver also transmits additional data to the hub, calculates metrics based on the standard deviation of an individual's circadian pattern (sleep/wake cycle) over a number of days, as well as overall variability of the circadian pattern using the IEM data and the additional data derived from the receiver.
16. The system of claim 1 , wherein the at least one IEM data system analyzes the additional data derived from the receiver and generates at least one metric based on the IEM data and the additional data derived from the receiver.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, analyzes additional data derived from the receiver (beyond just the IEM data) and generates metrics based on both the IEM data and the additional data from the receiver.
17. The system of claim 1 , wherein the additional data derived from the receiver comprises information regarding at least one of positional data, accelerometer data, dosing time, galvanic skin response, heat-flux, heart rate, or heart rate variability.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, uses additional data that includes positional data (GPS), accelerometer data (movement), dosing time, galvanic skin response (sweat), heat-flux, heart rate, or heart rate variability all derived from the receiver device.
18. The system of claim 1 , wherein the at least one metric comprises a circadian pattern of the individual.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, calculates metrics based on the individual's circadian pattern (sleep/wake cycle).
19. The system of claim 1 , wherein the at least one metric comprises blood pressure, blood pressure increase over time, and blood pressure increase compared to dosing and dose type.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, calculates metrics that include blood pressure, blood pressure increase over time, and blood pressure increase compared to dosing time and dose type.
20. The system of claim 1 , wherein the at least one IEM data system analyzes the IEM data such that the IEM data are time-normalized and interpolated to a time of day.
This system, where an ingestible device transmits data to a receiver, which sends it to a hub, which transmits it to an IEM data system for generating metrics and predictions, analyzes the IEM data by time-normalizing and interpolating it to a specific time of day. This allows for comparison of data across different days, regardless of when the ingestion event occurred.
21. A system comprising: an Ingestible Event Marker (IEM) device configured to collect IEM data from a body of an individual associated with the IEM device and transmit a conductive signal comprising the IEM data via body tissue, wherein the IEM data comprise information associated with an ingestion event, and the conductive signal is undetectable beyond the body of the individual; at least one IEM data system to process the IEM data, the at least one IEM data system comprising a processor and a non-transitory machine readable medium, wherein the non-transitory machine readable medium comprises instructions that when executed by the processor cause the processor to: analyze the IEM data and generates at least one metric based on the IEM data; generate predictive information based on the at least one metric, wherein the predictive information is related to prediction of a state of the individual; and wherein the IEM data is received from a body of an individual.
The system uses an ingestible event marker (IEM) device that transmits data about ingestion events (e.g., medication) from inside the body using a conductive signal that is undetectable outside the body. The IEM data system has a processor and memory. The memory contains instructions that, when executed, cause the processor to analyze the IEM data to generate metrics. The system then uses those metrics to predict a state of the individual, providing behavioral data and predictive inferences. The IEM data is received from the body of the individual.
22. The system of claim 21 , wherein the predictive information is associated with a likelihood of adherence to a medication regimen.
This system, where an ingestible device transmits data to an IEM data system for generating metrics and predictions, bases the predictive information on the likelihood that the person will adhere to their medication schedule.
23. The system of claim 21 , wherein the predictive information is associated with the prediction of occurrence of a future health event.
This system, where an ingestible device transmits data to an IEM data system for generating metrics and predictions, bases the predictive information on predicting the occurrence of future health events.
24. The system of claim 21 , wherein the predictive information is associated with a characterization of patient stability while on a medication regimen.
This system, where an ingestible device transmits data to an IEM data system for generating metrics and predictions, bases the predictive information on characterizing the patient's stability while on a medication regimen.
25. A system comprising: an ingestible event marker device configured to collect Ingestible Event Marker (IEM) data from a body of an individual and transmit a conductive signal comprising the IEM data via body tissue, wherein the IEM data comprise information associated with an ingestion event; a receiver adapted to be associated with the body of the individual, the receiver configured to receive the conductive signal comprising the IEM data via the body of the individual associated with the receiver, wherein the conductive signal is undetectable beyond the body; a hub to receive the IEM data; at least one IEM data system to receive the IEM data from the hub; and wherein the at least one IEM data system analyzes the IEM data and generates at least one metric based on the IEM data; and wherein the at least one IEM data system generates predictive information based on the at least one metric; and wherein the predictive information comprises an indication of a likelihood of adherence to a medication regimen.
The system uses an ingestible event marker (IEM) device that transmits data about ingestion events (e.g., medication) from inside the body using a conductive signal that is undetectable outside the body. A receiver worn on or near the body picks up this signal. A hub (e.g., a smartphone) receives the data from the receiver. An IEM data system then analyzes this data to generate metrics and uses those metrics to generate predictive information, that indicates the likelihood of the individual adhering to a medication regimen.
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March 15, 2013
March 28, 2017
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